Analysis, secure access to, and transmission of array images

ABSTRACT

Systems and methods are provided the autocentering, autofocusing, acquiring, decoding, aligning, analyzing and exchanging among various parties, images, where the images are of arrays of signals associated with ligand-receptor interactions, and more particularly, ligand-receptor interactions where a multitude of receptors are associated with microparticles or microbeads. The beads are encoded to indicate the identity of the receptor attached, and therefore, an assay image and a decoding image are aligned to effect the decoding. The images or data extracted from such images can be exchanged between de-centralized assay locations and a centralized location where the data are analyzed to indicate assay results. Access to data can be restricted to authorized parties in possession of certain coding information, so as to preserve confidentiality.

RELATED APPLICATION

Priority is hereby claimed to U.S. Provisional Application No.60/426,839, filed Nov. 15, 2002.

BACKGROUND

Recent rapid advances in molecular biology have created more demand forhigh volume testing based on the need to screen ever larger compoundlibraries, validate ever increasing numbers of genetic markers and testever more diversified patient populations. This has led to thedevelopment of new array formats, particularly for nucleic acid andprotein-protein interaction analysis, which increase parallel processingby performing requisite assays in a “multiplexed” format.

Conventionally, such assays are performed by producing arrays of nucleicacids and antibodies by way of “spotting” or “printing” of aliquotsolutions on filter paper, blotting paper or other substrates. However,notwithstanding their widespread current use in academic researchtargeting gene expression and protein profiling, arrays produced byspotting have shortcomings, particularly in applications placing highdemands on accuracy and reliability and where large sample volume andhigh throughput is required. In another more recently developedtechnique, spatially encoded probe arrays are produced by way of in-situphotochemical oligonucleotide synthesis. However, this technology islimited in practice to producing short oligonucleotide probes—andrequiring alternative technologies for the production of cDNA andprotein arrays—and precludes rapid probe array customization given thetime and cost involved in the requisite redesign of the photochemicalsynthesis process.

In addition to these inherent difficulties in assay performance,spatially encoded arrays produced by methods of the art generallyproduce data of such poor quality that specialized scanners are requiredto extract data of useable quality. Commercial systems available forthis purpose require confocal laser scanning—a slow process which mustbe repeated for each desired signal color—and limit the spatialresolution to ˜5 μm.

In order to resolve many of the problems associated with diagnostic andanalytical uses of “spotted arrays” of oligonucleotides and proteins (asoutlined in “Multianalyte Molecular Analysis Using Application-SpecificRandom Particle Arrays,” U.S. application Ser. No. 10/204,799, filed onAug. 23, 2002; WO 01/98765), arrays of oligonucleotides or proteinsarrays can be formed by displaying these capture moieties on chemicallyencoded microparticles (“beads”) which are then assembled into planararrays composed of such encoded functionalized carriers. See U.S. patentapplication Ser. No. 10/271,602 “Multiplexed Analysis of PolymorphicLoci by Concurrent Interrogation and Enzyme-Mediated Detection,” filedOct. 15, 2002, and Ser. No. 10/204,799 supra.

Microparticle arrays displaying oligonucleotides or proteins of interestcan be assembled by light-controlled electrokinetic assembly nearsemiconductor surfaces (see, e.g., U.S. Pat. Nos. 6,468,811; 6,514,771;6,251,691) or by a direct disposition assembly method (previouslydescribed in Provisional Application Ser. No. 60/343,621, filed Dec. 28,2001 and in U.S. application Ser. No. 10/192,352, filed Jul. 9, 2002).

To perform nucleic acid or protein analysis, such encoded carrier arraysare placed in contact with samples anticipated to contain targetpolynucleotides or protein ligands of interest. Capture of target orligand to particular capture agents displayed on carriers ofcorresponding type as identified by a color code produces, eitherdirectly or indirectly by way of subsequent decoration, in accordancewith one of several known methods, an optical signature such as afluorescence signal. The identity of capture agents including probes orprotein receptors (referred to herein sometimes also collectively as“receptors”) generating a positive assay signal can be determined bydecoding carriers within the array.

These microparticle arrays can exhibit a number of spectrallydistinguishable types of beads within an area small enough to be viewedin a microscope field. It is possible to achieve a high rate of imageacquisition because the arrays obviate the need for confocal laserscanning (as used with spotted or in-situ synthesized arrays) andinstead permit the use of direct (“snapshot”) multicolor imaging of theentire array under a microscope. If the system could be automatedfurther, such that, for example, the microscope is automaticallyrepositioned to optimally capture images from multiple arrays present ona multichip carrier and to positions optimizing decoding of the array,this would facilitate unattended acquisition of large data lots frommultiplexed assays.

In one format using microbead arrays, the encoding capacity of a chip(which includes several distinct subarrays) can be increased even whereusing the same set of color codes for the beads in each subarray. Whensubarrays are spatially distinct, the encoding capacity becomes theproduct of the number of bead colors and the number of subarrays.

In order to match the rates of data acquisition enabled by directimaging, rapid and robust methods of image processing and analysis arerequired to extract quantitative data and to produce encrypted andcompact representations suitable for rapid transmission, particularlywhere there is off-site analysis and data storage. Transmission of datashould be secure, and should be accessible only by authorized parties,including the patient but, because of privacy concerns, not to others.

SUMMARY

Disclosed are methods of increasing the confidence of the analysis, andfor rapid and automated decoding of encoded arrays used in assays, assaydata recorded in the form of images generated from arrays ofligand-receptor interactions; and more particularly, where differentreceptors are associated with different encoded microparticles(“beads”), and results are determined upon decoding of the arrays. Alsodisclosed are methods for transmitting and archiving data from suchassay arrays in a manner such that access is limited to authorizedpersons, and such that the chance of assigning one patient's results toanother are minimized. These methods are particularly useful whereassays are performed at decentralized user (“client”) sites, because themethods permit secure exchange of data between the client and a centralfacility (“information keeper”), where the data can be centrally decodedand analyzed so as to provide greater reliability, and then archived ina restricted manner where only authorized users have access.

In a centralized regime, patient samples, collected in the field, aresent for analysis to a central location, where they are assayed. Resultsare provided to authorized users by remote transmission. Users, whilerelieved of any responsibility relating to assay completion and dataanalysis, are faced with the loss of control over the assayimplementation and analysis and may face the inconvenience ofsignificant delay. Non-standard assays may be unavailable orprohibitively expensive. In addition, this service ordinarily will notbe suitable for perishable samples, or large collections of samples,such as those created in a pharmaceutical research laboratory.

In a decentralized paradigm, analytical instrumentation, such asmicroplate readers complete with all requisite software, are distributedto users who perform assays, record results, and may also performsubsequent data analysis. Alternatively, assay results may betransmitted to a central facility for decoding, analysis, processing andarchiving, and such centralized procedures may provide greaterreliability.

The analysis server model (useful, inter alia, for moleculardiagnostics), as disclosed herein, expands upon these paradigms bycombining decentralization of assay performance with centralized dataanalysis. That is, while assay performance and data generation are atuser facilities, critical aspects of subsequent data analysis andrelated services may be performed in a centralized location which isaccessible to authorized users in a two-way mode of communication viapublic or private computer networks. The analysis server model can beapplied to assays performed in a highly parallel array format requiringonly a simple imaging instrument, such as a microscope, to recordcomplex assay data, but requiring advanced methods of analysis andmathematical modeling to reliably process and analyze assay data.Images, recorded at a user location, are uploaded to a centralizedlocation where such analysis are performed, results being made availableto authorized users in real time.

The methods and processes are further explained below with reference tothe drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 displays a 1×8 multichip carrier, where chips with encoded arrayson a surface are housed in the carrier's wells.

FIG. 2 is a flowchart of an imaging system, including image acquisition,a decoder and a reader for image processing, and an analyzer for dataanalysis.

FIG. 3 is a flowchart illustrating an exemplary system where assay datacan be decoded and centrally analyzed.

FIG. 4 is a flowchart illustrating viewing a multiplexed assay andrecording of the assay image.

FIG. 5 is a flowchart depicting sample collection, performing of assaysand acquisition of an assay image.

FIG. 6 is a flowchart of the formation of a decoding image and decodingdata records where the resulting information can be stored in adatabase.

FIG. 7 is a diagram illustrating the, reading and processing of an assayimage and recording of an assay data record, where the resultinginformation can be stored in a database.

FIG. 8 is a flowchart illustrating the recording, analysis and decodingof assay data, and combining with an assay data record to generate adecoded assay data record, where the resulting information can be storedin a database.

FIG. 9A shows a grid (with extra rows and columns) in unshifted position(0,0).

FIG. 9B shows the grid of FIG. 9A and in a configuration shifted toposition (1,1).

FIG. 9C illustrates the seven possible shifting positions for the gridof 9A and 9B.

FIG. 10A is an illustration of a two-party, multiple-user, dataexchange.

FIG. 10B illustrates a three-party data exchange, where one party is thepatient.

FIG. 10C illustrates more specifically a secure data transaction betweenthree parties, where one party is the patient.

FIGS. 11A to 11D depict the DECODER Graphical User Interface (GUI)illustrating steps in the course of processing a decoding image.

FIG. 12 illustrates a constructed Decoding Map.

FIG. 13 shows a READER Graphical User Interface (GUI) illustrating stepsin the course of processing an assay image.

FIG. 14 illustrates a decoded assay data record in histogram form.

FIG. 15A illustrates mesh overall orientation with the shortest pathalgorithm, wherein L_(h) ^(ref) and L_(v) ^(ref) are the referencelines. By applying shortest path, L_(h) and L_(v) are found. It can beseen that when L_(h) is shorter than L_(v), then the overall orientationof the mesh is 0 degrees.

FIG. 15B illustrates vertical line grid partition by the shortest pathalgorithm.

DETAILED DESCRIPTION

FIG. 1 depicts a multichip carrier (100) displaying a barcode (110)along with a set of 2×2 chips (120) in each of eight positions, eachsuch chip comprising one or more random array(s) of encodedmicroparticles (130). The decoding of the microparticles 130 andanalysis and transmission of the data can be performed in accordancewith the methods described herein. Each of the 2×2 chip subarrays (i.e.,four chips) correspond with one individual patient sample. Thus, given aset of 128 distinguishable colors for the beads in the array, this canproduce an array complexity of 4×128=512 for each individual. The 1×8sample format shown in FIG. 1 with a center-to-center distance of 9 mmmatches the standard 8×12 microwell format and is readily scaled to n×8for high-throughput sample handling (as described further in U.S. patentapplication Ser. No. 10/192,352).

As illustrated in FIG. 2, the imaging system comprises hardware foracquiring a decoding image (200), means for acquiring an assay image(210), a decoder (220), a reader (230), and an analyzer (240). Acquiringand recording a decoding image or an assay image can be accomplishedusing the system shown in FIG. 4.

Molecular interaction analysis on a random array of encodedmicroparticles or beads, where each subpopulation of such encoded beadsdisplays a unique receptor molecule, for example, an oligonucleotide, orprotein molecule, can be performed using these systems. Each suchreceptor is designed so as to be able to form a molecular complex with acognate target analyte or ligand, the formation of a complex resultingin an optical signature (for example, the ligand can be fluorescentlylabeled and thus detectable following complex formation) that isdetected by acquiring and analyzing images of the array. Assay resultstake the form of an image comprising a set of intensities, each of whichis uniquely associated with one receptor-ligand interaction in thearray. Such assays are well-suited for DNA and protein analysisperformed in a multiplexed or parallel mode using an array format,including applications such as genetic expression profiling,polymorphism and mutation analysis, protein-protein analysis includingantibody-antigen interaction analysis, organic compound-receptorinteractions, and further including all those disclosed inPCT/US01/20179, U.S. Pat. Nos. 6,251,691; 6,387,707 and U.S. patentapplication Ser. No. 10/271,602, all of which are incorporated herein byreference.

The decoding of encoded arrays and the analysis, interpretation orstorage of assay results requiring access to methods and algorithms oraccess to databases which are not available at the location housing theanalytical instrumentation, but are instead available at a remotelocation, is addressed herein. Special consideration is given tosituations in which these operations, or parts thereof such as thecollection and preparation of a sample, the actual assay or assays ofinterest and additional operations such as interpretation orconsultation are performed in separate locations. In one embodiment, themethods and apparatus described herein permit complex multianalyteanalysis to be performed in locations visited by a patient whose sampleis collected and analyzed on site, while providing transaction protocolsto perform data analysis and optional additional services, such as datainterpretation and archiving to be performed off-site. The methodsherein permit the secure exchange of information recorded on site andanalyzed at the site of an application service provider.

Accordingly, the systems and methods provided herein for the analysis ofclinical samples or other analytes provide for communication among three(or optionally more) participants, including: (i) the sample originator,for example a patient seeking clinical diagnosis or a biomedicalresearch laboratory seeking analytical services; (ii) the analysisprovider (which may or may not also be the tester performing the assay);and (iii) the tester, which performs the assay but is generally providedminimal information about assay outcomes, decoding, or sampleorigination; and (iv) an optional intermediary, for example a samplecollector and/or processor or communicator of results, such as acounselor.

These methods allow the rapid assaying and analysis of customized randomencoded bead arrays, where a multiplexed assays are performed on patientsamples, and multiple assays may be conducted. Suitable panels mayinclude, for example, a tumor marker panel including antigens such asPSA and other suitable tumor markers, an allergy panel, a pregnancypanel comprising tests for human chorionic gonadotropin, hepatitis Bsurface antigen, rubella virus, alpha fetoprotein, 3′ estradiol andother substances of interest for monitoring a pregnant individual; ahormone panel, an autoimmune disease panel including tests forrheumatoid factors and panel reactive antibodies and other markersassociated with autoimmune disorders, a panel of blood-borne viruses anda therapeutic drug panel comprising tests for Cyclosporin, Digoxin andother therapeutic drugs of interest.

In addition, such panels also may include, for example, oligonucleotideprobes designed for nucleic acid analysis including analysis of cDNApanels for gene expression profiling, oligonucleotide probe panelsdesigned for the multiplexed analysis of mutations causing geneticdiseases such as cystic fibrosis, Tay-Sachs disease, Ashkenazi Jewishdiseases including Gaucher disease and others, the analysis ofpolymorphisms such as those in the Human Leukocyte Antigen complex whichdetermine the degree of compatibility between donor and recipient intransplantation of bone marrow or solid organs, a blood antigen panelfor blood typing, the analysis of chromosomal aberrations such as thoseunderlying Down Syndrome and others surveyed in prenatal screening orcertain blood-borne cancers such as certain leukemias. The multiplexednucleic acid analysis involved in assaying of these panels can beperformed using either hybridization-mediated detection orhybridization-mediated elongation-mediated detection, as described inU.S. patent application Ser. No. 10/271,602, entitled: “MultiplexedAnalysis of Polymorphic Loci by Concurrent Interrogation andEnzyme-Mediated Detection” filed Oct. 15, 2002.

In either hybridization-mediated detection or hybridization-mediatedelongation-mediated detection, an association of polynucleotide in thesample with a probe oligonucleotide on a bead results in an assay signalin the form of an optical signature. For example, in the READ™ format,each encoded bead within the array (where each bead has multiple probesattached thereto) may produce one or more of such optical signatureswhich are able to be recorded by the systems of the invention. Theoptical signature can be a fluorescence signature. Optical signatures ofinterest include, without limitation, luminescence includingbioluminescence, chemiluminescencve and electrochemiluminescence. Directvisual signatures resulting, for example, from the transformation of theassay locus, for example, by agglutination of multiple beads, or theattachment of marker particles to assay loci, can also be recorded andanalyzed using the methods set forth herein.

I. Automated High-Throughput Array Imaging for Molecular InteractionAnalysis

I.1. Assay and Decoding Data Records

FIG. 3 is an illustration of the methods to conduct the analysis ofanalytes using bead arrays assembled on substrates, according to theREAD process of multiplexed analysis. The method of FIG. 3 involvesproducing a bead array (300), obtaining a decoding image (310),processing the decoding image using a decoder (320), and obtaining adecoding data record (330). In parallel, an assay is performed using thebead array (340) to obtain an assay image (350), where the assay imageis processed using a reader (321) and the assay data is recorded (360).The decoding data and assay data are then combined and the image isanalyzed (370). The decoding image may represent a combination ofseveral distinct images recorded in separate spectral bands or colorchannels, where the encoding is with chemically and/or physicallydistinguishable characteristics that uniquely identify the binding agentdisplayed on the bead surface. For example, when the uniquelydistinguishable characteristic is color, a “decoding image” of the beadarray (where the bead are immobilized on the substrate) is recorded toreference the color code of constituent beads, where the color codeuniquely corresponds to the chemical identity of the binding agentsdisplayed on each individual bead's surface.

Referring to FIG. 3, the decoding image (310) may be generated followingcompletion of array assembly by the array manufacturer at themanufacturing site or it may be generated by the user of the array inconnection with the completion of a bioassay or other chemical test,either prior to, or subsequent to completion of the assay or test at thetest site. The decoding image (310) becomes part of the decoding datarecord (330), which also contains a variety of identifiers for reagent,microparticle and substrate batch and lot numbers. The decoding image(310) can be generated following array assembly at the manufacturingsite and processed to create a decoding data record (330) which isstored in a database on a central server. Access to the decoding datarecord, or parts thereof, can be accessible in the form of a copy of therecord, for example, of copies on a recording medium such as a CD thatis distributed along with arrays and multichip carriers or cartridges.Alternatively, access to the decoding data record, or parts thereof, ismade accessible in the form of authenticated database accessible only toauthorized users as disclosed herein.

Following completion of an assay (340) at a user site, the assay imageis recorded (350) and an Assay Data Record (360) is created which servesto record the optical signature(s) indicating the binding of ligandmolecules to immobilized receptors. For example, when fluorescence isselected to provide the optical signature of interest, the fluorescenceintensity recorded from each position within the array indicates theamount of complex formed in that location by receptor and ligand bindingor hybridization. Multiple modes of generating such optical signaturesinclude the direct or indirect labeling of target analytes (for example,by using fluorescent primers to conduct PCR of genomic regions to beassayed) or the introduction of fluorescence by way of probe elongationusing labeled nucleotides. See, e.g., U.S. patent application Ser. No.10/271,602 “Multiplexed Analysis of Polymorphic Loci by ConcurrentInterrogation and Enzyme-Mediated Detection.” The assay image forms theassay data record.

As described herein below, the methods herein can be used for processingdecoding and assay images, for extracting representations suitable forrapid transmission and for rapidly and reliably combining decoding andassay image signatures so as to associate assay results recorded fromspecific array locations with corresponding chemically encoded probeidentities.

Assembly of Random Encoded Bead Arrays. Random encoded arrays may beassembled by the methods described in U.S. Pat. No. 6,251,691, or inU.S. patent application Ser. No. 10/192,352, entitled “Arrays ofMicroparticles and Methods of Preparation Thereof,” incorporated hereinby reference in its entirety. These methods combine separate batchprocesses that respectively serve to produce application-specificsubstrates (e.g., chips at the wafer scale) and encoded bead librarieswhose constituent beads are functionalized (e.g., at the scale of ˜10⁸beads/100 μl of suspension) to display receptors, such as nucleic acidsand proteins of interest. Beads assembled in an array may be immobilizedby physical or chemical means to produce fixed random encoded arrays.

In addition, the methods described in U.S. Pat. No. 6,251,691 may beused to form multiple bead arrays. Alternatively, multiple bead arrayscan be formed simultaneously in discrete fluid compartments maintainedon the same chip. The integration of array assembly with microfluidicsproduces a self-contained, miniaturized, optically programmable platformfor parallel protein and DNA analysis. Once formed, these multiple beadarrays may be used for concurrent processing of multiple samples.

Spatial encoding of multiple arrays also can be accomplished byassembling planar bead arrays in a desired location, using discretefluid compartments or the assembly methods described in U.S. Pat. No.6,251,691. Alternatively, spatial encoding can be accomplished byassembling separate chips, each carrying at least one random encodedarray drawn from a specific pool, into a designated configuration ofmultiple chips.

Chemical Encoding and Functionalization of Beads. Chemical encoding maybe accomplished by staining beads with sets of optically distinguishabletags, such as those containing one or more fluorophore dyes spectrallydistinguishable by excitation wavelength, emission wavelength,excited-state lifetime or emission intensity. Two-color and three-colorcombinations, where the latter may be constructed as “stacked” two-colorcombinations, are decoded as described herein.

The optically distinguishable tags may be used to stain beads inspecified ratios, as disclosed, for example, in Fulwyler, U.S. Pat. No.4,717,655, which is incorporated herein by reference in its entirety.Staining may also be accomplished by swelling of particles in accordancewith methods known to those skilled in the art (see, e.g., Molday,Dreyer, Rembaum & Yen, J. Mol Biol 64, 75-88 (1975); L. Bangs, “Uniformlatex Particles, Seragen Diagnostics, 1984). Beads can be encoded byswelling and bulk staining with two or more colors, each individually atseparate intensity levels, and mixed in separate nominal molar ratios inaccordance with methods known to the art. See also U.S. patentapplication Ser. No. 10/348,165, entitled: Method of Controlling SoluteLoading of Polymer Microparticles; filed Jan. 21, 2003. Combinatorialcolor codes for exterior and interior surfaces is disclosedPCT/US98/10719, which is incorporated herein by reference.

Beads to be used in the bead arrays of the invention for biomolecularanalysis are functionalized by a binding agent molecule attachedthereto, where the molecule may be, for example, DNA (oligonucleotides)or RNA, fragments, peptides or proteins, aptamers and small organicmolecules attached in accordance with processes known in the art, e.g.,with one of several coupling reactions of the known art (G. T.Hermanson, Bioconjugate Techniques (Academic Press, 1996); L. Illum, P.D. E. Jones, Methods in Enzymology 112, 67-84 (1985)). The binding agentmolecule may be covalently attached to the bead. Beads may be stored ina buffered bulk suspension until needed.

Functionalization typically may be performed, for example, with aone-step or two-step reaction which may be performed in parallel usingstandard liquid handling robotics and a 96-well format to covalentlyattach any of a number of desirable functionalities to designated beads.Beads of core-shell architecture may be used (as described in U.S.Provisional Application entitled: “Ionic Gel-Shell Beads with Adsorbedor Bound Biomolecules,” filed Oct. 28, 2003, and applications claimingpriority thereto) where the shell is a polymeric layer. Samples may bedrawn along the way for automated QC measurements. Each batch of beadspreferably has enough members such that chip-to-chip variations withdifferent beads on chips are minimized.

Beads may be subjected to quality control (QC) steps prior to arrayassembly, for example, the determination of morphological and electricalcharacteristics, the latter including surface (“zeta”) potential andsurface conductivity. In addition, assays may be performed on beads insuspension before they are introduced to the substrate, to optimizeassay conditions, for example, to maximize assay sensitivity andspecificity and to minimize bead-to-bead variations. QC steps forsubstrates may include optical inspection, ellipsometry and electricaltransport measurements.

Substrates. Substrates, e.g., silicon wafers and chips, are used whichmay be patterned by invoking standard methods of semiconductorprocessing, for example to implement interfacial patterning methods ofLEAPS by, e.g., patterned growth of oxide or other dielectric materialsto create a desired configuration of impedance gradients in the presenceof an applied AC electric field. See U.S. Pat. No. 6,251,691. Patternsmay be designed so as to produce a desired configuration of ACfield-induced fluid flow and corresponding particle transport, or totrap particles in wells, as described in US Provisional Applicationentitled: Immobilization of Bead-displayed Ligands on SubstrateSurfaces,” filed Jun. 12, 2003, Ser. No. 60/478,011.

In addition, substrates may be compartmentalized by depositing a thinfilm of a UV-patternable, optically transparent polymer to affix to thesubstrate a desired layout of fluidic conduits and compartments toconfine fluid in one or several discrete compartments, therebyaccommodating multiple samples on a given substrate. Other substratessuch as patternable or machinable ceramics also are suitable.

Customization by Pooling. Bead-displayed probes of interest can beselected from a library of beads and pooled prior to array assembly.That is, customization of assay composition is achieved by selectingaliquots of designated encoded beads from individual reservoirs inaccordance with a specified array composition. Aliquots of pooledsuspension are dispensed onto a selected substrate (e.g., a chip). Thealiquots may be mixed or may be separated to form a multiplicity ofplanar random subarrays of encoded beads, each subarray representingbeads drawn from a distinct pool. The array may be laid out in a mannersuch that aliquot positions in the array correspond to the identity ofeach aliquot of the pooled bead population.Array Analysis. The binding interaction between the receptor (which maybe an oligonucleotide) displayed on color-encoded functionalized beadsand a ligand (or “analyte”) may be analyzed after a random encoded beadarray is assembled in a designated location on the substrate or chip.For example, bead arrays may be formed after completion of the assay,subsequent to which an assay image and a decoding image may be taken ofthe array.Immobilization Microparticle arrays may be immobilized by mechanical,physical or chemical anchoring as described in PCT/US01/20179(counterpart of U.S. patent application Ser. No. 10/192,352), includingby trapping particles in wells, as described in US ProvisionalApplication entitled: Immobilization of Bead-displayed Ligands onSubstrate Surfaces,” filed Jun. 12, 2003, Ser. No. 60/478,011.

In certain embodiments, bead arrays may be immobilized by physicaladsorption mediated by application of a DC voltage, set to typically <5V(for beads in the range of 2-6 γm, a gap size of 100-150 γm, and asilicon oxide layer of ˜100 Angstrom thickness). Application of such aDC voltage for <30 s in “reverse bias” configuration—so that an n-dopedsilicon substrate would form the anode—causes bead arrays to bepermanently immobilized. See U.S. Pat. No. 6,251,691.

In certain embodiments, the particle arrays may be immobilized bychemical means, e.g, by forming a composite gel-particle film. In oneexemplary method for forming such gel-composite particle films, asuspension of microparticles is provided which also contain allingredients for subsequent in-situ gel formation, namely monomer,crosslinker and initiator. The particles may be assembled into a planarassembly on a substrate by application of the LEAPS™ process, asdescribed in U.S. Pat. No. 6,251,691. Following array assembly, and inthe presence of the applied AC voltage, polymerization of the fluidphase is triggered by thermally heating the cell ˜40-45° C. using an IRlamp or photometrically using a mercury lamp source, to effectivelyentrap the particle array within a gel. Gels may be composed of amixture of acrylamide and bisacrylamide of varying monomerconcentrations, from about 20% to 5% (acrylamide:bisacrylamide=37.5:1,molar ratio), or, in the alternative, any other low viscosity watersoluble monomer or monomer mixture may be used as well. In one example,thermal hydrogels are formed using azodiisobutyramidine dihydrochlorideas a thermal initiator at a low concentration ensuring that the overallionic strength of the polymerization mixture falls in the range of ˜0.1mM to 1.0 mM. The initiator used for the UV polymerization is Irgacure2959® (2-Hydroxy-4′-hydroxyethoxy-2-methylpropiophenone, Ciba Geigy,Tarrytown, N.Y.). The initiator is added to the monomer to give a 1.5%by weight solution. The methods described in U.S. patent applicationSer. No. 10/034,727 are incorporated herein by reference.

In certain embodiments, the particle arrays may be immobilized bymechanical means, for example, such arrays may be placed into an arrayof recesses may be produced by standard semiconductor processing methodsin the low impedance regions of the silicon substrate. The particlearrays may moved into the recesses by, e.g., utilizing LEAPS™-mediatedhydrodynamic and ponderomotive forces to transport and accumulateparticles in proximity to the recesses. The A.C. field is then switchedoff and particles are trapped into the recesses and mechanicallyconfined. Excess beads are removed leaving behind a geometricallyordered random bead array on the substrate surface.

Carriers and Cartridges. Substrates (e.g., chips) with immobilized beadarrays may be placed in distinct enclosed compartments, and samples andreagents may be transported in and out of the compartments by means offluidic interconnection. On-chip immunoassays, including those forvarious cytokines, e.g., interleukin (IL-6) may be performed in thisformat. In such immunoassays, samples are allowed to react with beadsimmobilized on the chip and adsorption of targets in the samples by thereceptors on the beads may be detected by binding of fluorescentlylabeled secondary antibodies.

Random Encoded Array Detection. Once the functionalized and encodedbeads are prepared and assembled on the substrate, a binding assay maybe performed. The array can function as a two-dimensional affinitymatrix which displays receptors or binding agents (e.g.,oligonucleotides, cDNA, aptamers, antibodies or other proteins) tocapture analytes or ligands (oligonucleotides, antibodies, proteins orother cognate ligands) from a solution or suspension that is brought incontact with the array. The bead array platform may be used to performmultiplexed molecular analysis, such as, e.g., genotyping, geneexpression profiling, profiling of circulation protein levels andmultiplexed kinetic studies, and may be used for the implementation ofrandom encoded array detection (READ™), including analysis based onimage acquisition, processing and analysis.

I.2 Multicolor Image Acquisition Using an Automated Array Imaging System

Multicolor images can be used to encode and display information recordedin two or more color channels. The construction of multicolor images canbe accomplished by merging two or more images recorded in separatespectral bands and distinguished by selection of suitable color filtercombinations, as is well-known in the art.

The READ™ format provides for multicolor images before and after theassay, referred to respectively as a decoding image and an assay image.The decoding image serves to record the location of particularidentified solid phase carriers—and hence the identity of receptorsdisplayed on such carriers—based on their color-encoding in an array.These solid phase carriers may be color encoded using, for example,combinations of two or more fluorescent dyes. The assay image reflectsthe optical signatures induced by association of target analytes withcarrier-displayed receptors. In one example, useful in gene expressionprofiling, signal produced on an array by the hybridization of cDNA orRNA produced from a tissue sample of interest and labeled withfluorescent dye (e.g., Cy5) may be compared with the signal producedfrom a known quantity or concentration of cDNA or RNA produced from areference sample and labeled with a fluorescent dye, (e.g., Cy3 or Cy5).The comparison of signal from the tissue sample and the reference sampleindicates the level of gene expression. An analogous format may be usedin molecular cytogenetics applications. Protein-protein interactions canalso be monitored with this format, where the protein in the sample islabeled following its association with the bead-bound protein in thearray, for example, by using a labeled antibody which targets theprotein.

Multicolor images obtained from monitoring of random encoded arrays canbe recorded automatically. The arrays can be formed on chips, andmultiple chips can be placed on a carrier, such as that shown in FIG. 1.However, alternative modes of presenting and arranging random encodedarrays are possible. For example, samples also may be mounted in flowcells or cartridges permitting fluidic operation so as to injectsamples, reagents and buffers, permitting imaging of probe array by wayof standard microscope optics.

The components and subsystems of an exemplary image acquisition systemmay include the following:

-   -   Input/Output File System: images are handled in TIFF format,        other files are handled in XML (eXtensible Markup Language)        format; an XML output file records the settings of parameter        such as image acquisition integration time, filter selection    -   System Status: illumination source (ON/OFF), stage target        position    -   Mechanical Subsystems: xy translator, z actuator, filter wheel,        ND filters

-   Barcode Reader

-   Image Acquisition and Storage

-   Control Software

-   Graphical User Interface (GUI)

-   Autocenter Function (implemented by Software)

-   Autofocus Function (implemented by Software)    Hardware. FIG. 4 shows a microscope, for use in an imaging system,    providing transmission or reflection geometry and multiple methods    of generating optical contrast. In one embodiment, reflection    geometry is chosen to record “brightfield” (e.g., an image not    recorded under fluorescence optical contrast imaging conditions,    including an image recorded under white light illumination) as well    as multicolor fluorescence images in a fully automated manner.

Other components and subsystems of an imaging system are set forthbelow.

Illumination Source. Depending on the application of interest, anysuitable illumination source (400) can be used, including a laser, or astandard microscope illumination sources including tungsten halogen,mercury and xenon. In one embodiment, a xenon light source is used formulti-fluorescence imaging. A mechanical shutter (410) controls“Light-ON” and “Light-OFF” functions.Mechanical Subsystems. Certain subsystems can be used to controlprecision positioning of the sample (420), selection of image mode,namely brightfield or (epi)fluorescence (using different filters), andspectral filter selection. The positioning of the sample involveshorizontal and vertical sample positioning, deploying acomputer-controlled xy-translator (part of the xyz stage (430)), whichcan be under the control of a manual “joystick” positioning function oran automated autocentering function, and a z-actuator connected to avertical motion of the sample under control of an autofocusing functionwhich may be computer-controlled. Fluorescence filter combinations canbe selected automatically using a computer controlled carousel housingfilter cubes (440).Barcode Scanner. A handheld barcode scanner (450) can be used to scan anidentifyng barcode affixed to each multichip carrier or other samplecarrier or cartridge. The barcode can identify, for example, thecomposition of beads associated with the carrier, the origin of thecarrier (i.e., the batch it is derived from) or other information.Optical Subsystem—A combination of microscope objective (460) andcollection optics (470) in standard configuration, or in Koehlerconfiguration, is used for illumination as well as collection and imageformation.CCD Camera. A CCD camera (480), preferably with C-mount, is attached tothe microscope to record images.Control Software. The fully automated operation of the array imagingsystem is enabled by control software (also referred to herein as theArray Imaging System—Operation Software (“AIS-OS”)) comprising aGraphical User Interface (GUI) as well as control algorithmsimplementing autocentering and autofocus functions, as set forth below.Operation of Array Imaging System. Bead arrays mounted in a multichipcarrier or cartridge are placed on the translation stage of the ArrayImaging System (“AIS”) and multicolor images (both decoding and assayimages) are recorded. Table I below shows the pseudocode for thetranslation/decoding operation of the Array Imaging System.

TABLE I IF (record Decoding Image mode) {   LoadFile (Production DataRecord); /** Load or Create **/   ScanBarCode (Carrier ID);   WriteInfo(Carrier ID, Number of BeadChips on Carrier,   ProductionDataRecord);  }; ELSE IF (record Assay Image mode) {   ScanBarCode(Carrier ID);  ReadInfo (Production Data Record, CarrierID, Number   ofBeadChips on  Carrier);         / ** The Production Data Record is accessible as an        XML file in the folder set up by the AIS for each         sample**/   ConstructFileName (AssayDataRecord)  /** to match   ProductionData   Record **/   WriteInfo(Assay Data Record, CarrierID, Number ofBeadChips   on Carrier);         / ** Production Data Record and AssayData Record         are maintained on an SQL database server accessible        to authenticated users **/ }; index = 0; WHILE( index < Numberof BeadChips on Carrier) {   IF (record Assay Image mode) {    ReadTargetPosition(Production Data Record,     BeadChipID, X, Y, Z);  };   MoveXYTranslator ( X, Y );MoveZTranslator (Z);   Z =AutoFocus(Z);      /** perform autofous operation **/   AutoCenter(X,Y);    /** In record Decoding Image mode,             operator setsfirst center position **/   Z = AutoFocus(Z);      /** repeat autofocus**/   IF (record Decoding Image mode) {   WriteTargetPosition(ProductionData Record, BeadChipID, X, Y, Z);   };   FOR (each desiredColorChannel) {     SetFilter (ColorChannel);     AcquireImage ();      /** See details below **/     WriteImageFile(CarrierID,BeadChipID, ColorChannel,     ImgFile);     IF (record Decoding Imagemode) {       UpdateProductionDataRecord(CarrierID, BeadchipID,      ImgFileName);     };     ELSE IF (record Assay Image mode) {      UpdateAssayDataRecord(CarrierID, BeadChipID,       ImgFileName);    };   };   index++; };Autocentering. The autocentering function, using a given input image,positions the XY translation stage so as to place each selected arrayinto the center of the imaging system's field view by determining thatthe image in the viewing field is, in fact, a rectangle with the correctnumber of sides and right angle corners. This is accomplished byperforming the steps in Table II, which shows the pseudocode for theautocentering operation:

TABLE II OpenImg(InputImg, OpenedImg);  /** apply sequence ofmorphological erosion and dilation operations to eliminate internalstructure of the image showing particle array **/Binarize(OpenedImg,BinImg); /** apply optimal thresholding algorithm**/. CloseImg (BinImg, ClosedImg); /** apply sequence of morphologicaldilation and erosion operations **/ AnalyzeConnectivity (ClosedImg,ConComp); /** find connected components in closed image **/ Filter(ConComp, FilteredConComp); /** filter out all “non- box-like” regions;a “box-like” region is defined as a region whose area is close to thearea of its “bounding box” **/ Center = FindMaxConComp(FilteredConComp);/** find largest connected component that is smaller than 70% of theimage size and find its centroid **/ MoveXYTranslator ( Center.X,Center.Y ); /** position stage **/Only when a new multichip carrier (“MCC”) is first inspected anddecoded, does the positioning of the very first array on the MCC requireinteractive operation. This initial positioning step is performed as apart of array assembly or subsequent quality control. All subsequentpositioning may be automatic. The full processing-positioning followedby acquiring of multiple images and displaying a rendering of amulticolor image typically requires only a few minutes.Autofocusing. The autofocusing function positions the Z actuator so asto bring the image in focus and place each selected array into thecenter of the imaging system's field view. An algorithm which uses alocal contrast function to determine optimal focus can be used. Thislocal contrast is determined as follows using a fast computation:evaluate, for each pixel, a quantity Δ_(max), defined as the largestabsolute value of the difference in intensity between that pixel and itsfour horizontal and vertical neighbors; next, sum the Δ_(max) over adesignated portion of the image: this serves to speed up the operation.The Z-position of maximal contrast is located. The autofocus functionshould help ensure vertical positioning to within one micron or less.I.3 Performing Multi-Analyte Molecular Analysis

The process of performing multianalyte molecular analysis using thesystem herein would, in an exemplary embodiment, involve theconcatenation of the previously described operations as follows (FIG.5):

-   Collect patient sample (510)-   Transfer to sample container, preferably a barcoded sample container    (520)-   Process sample (530) using requisite reagents (540) to produce    analyte (550)-   Select multichip carrier (MCC), obtain MCC information (560) (580),    for example, positions of beadchips arranged on MCC;-   Perform assay (590) to produce transformed analyte (591) to be    analyzed-   Mount MCC in array imaging system and read MCC barcode (570) to    obtain assay configuration-   Acquire assay image(s) (592)-   Submit assay image data for processing and analysis, details of    which are described below.    II Image Processing and Analysis

Processing, analyzing, transmitting and storing images as set forthherein can be implemented in Visual C++ (MicroSoft) using a graphicaluser interface software package including .exe files implementing thepseudocodes and flow diagram steps described herein including, forexample, the analysis, processing and decoding steps described herein.

An image processing program (designated “DECODER”), which can be run,for example, on the Microsoft Operating system, e.g., Windows 98 or2000, and which contains functions to display, process, save and print“multicolor” sets of multiple microarray images in an integratedgraphical user interface (GUI) and to generate a decoding data recordwhich may be submitted for further analysis to the ANALYZER, residingeither on the same computer or on a separate computer. This program canbe readily implemented by those skilled in the art, using the outlinesherein. As illustrated in FIG. 6, the operation of the DECODER comprisesreading the Decoding Image Record (610, 620), rendering and displayingdecoding image(s) (630), and processing decoding image(s) (640) tocreate a Decoding Data Record (650), display scatter plots (660) andupdate databases (670).

Another image processing program (designated READER), which can be run,for example, on the Microsoft Operating system, e.g., Windows 98 or2000, and functions to display and to process pairs of assay imagesacquired so as to generate an assay data record which may be submittedfor further analysis to the ANALYZER, residing either on the samecomputer on a separate computer. As illustrated in FIG. 7, the operationof the READER comprises reading the Assay Image Record (710, 720),rendering and displaying assay image(s) (730), and processing assayimage(s) (740) to create an Assay Data Record (750) and update databases(760).

ANALYZER is an analysis program. As illustrated in FIG. 8, ANALYZERreceives input from DECODER, in the form of a Decoding Data Record(810), and READER, in the form of an Assay Data Record (820), andcomprises functions to perform further analysis including: clusteranalysis (830) to create a decoding map (840). The decoding map iscombined (850) with an assay data record so as to produce a DecodedAssay Data Record (860) which is displayed (870) in a variety of formatsand stored in a database (880).

ANALYZER, DECODER and READER may reside on separate computers which maycommunicate by way of a data network. In this manner, data from assayscan be received from a remote site but can be decoded and analyzed atanother site. In such embodiment, DECODER and ANALYZER may be integratedinto a single program or loaded onto the same computer.

IChipReader provides a COM interface in the form of a dynamically linkedlibrary linking the functions of DECODER and READER.

This image analysis system has the following advantages.

Reliability: Robust algorithms have been designed in order to handleimages of widely varying quality encountered in practice, includingimages exhibiting very low contrast or variations in contrast across theimage, significant noise and corruption of edges or features, ordisplacement and misalignments between multiple images of a given array.These robust algorithms ensure the reliability of the results producedby the analysis.

Accuracy: The entire sequence of processing steps is performed withouthuman intervention, thereby avoiding error and enhancing ease-of-use.

Speed: Algorithms have been designed for efficiency, and functions havebeen integrated so as to minimize processing time. A chip displaying asingle array can be processed in as little as 4 seconds.

Productivity and throughput: Sets of images may be analyzed in batchmode.

Ease-of-use and convenience: The GUI package provides convenience andflexibility in controlling all system functions.

Functions and capabilities provided by these systems includingprocessing, analyzing, transmitting and storing images are elaboratedbelow.

II.1 Array Segmentation and Extraction of Signal Intensities

Image processing may be applied to each of the one or more constituentimages of a composite image in the decoding data record or assay datarecord to segment the image and extract a textual representation of thesignal intensity distribution within the array. This representationwould serve as input for further analysis.

In certain embodiments, decoding and assay images or the correspondingdata records are analyzed to obtain quantitative data for each beadwithin an array. The analysis invokes methods and software implementingsuch methods to: automatically locate bead arrays, and beads withinarrays, using a bright-field image of the array as a template; groupbeads according to type; assign quantitative intensities to individualbeads; reject processing “blemishes” such as those produced by “matrix”materials of irregular shape in serum samples; analyze backgroundintensity statistics; and evaluate the background-corrected meanintensities for all bead types along with the corresponding variances.

II.1.1 Referenced Arrays

Referenced arrays are located in designated positions, and in designatedorientations, with respect to features designed into patternedsubstrates in accordance with methods previously disclosed inPCT/US01/20179, U.S. Pat. No. 6,251,691 and U.S. patent Ser. No.10/192,352. For example, a locus of low impedance on a substrate may bedesigned to collect particles using the LEAPS™ method and may furthercontain a central recess grid to mechanically immobilize microparticles.See U.S. Pat. No. 6,251,691.

Specifically, in one embodiment, following completion of AutoCenteringand AutoFocusing as described above, the system makes a record, in boththe “record Decoding Image” and the “record Assay Image” modes, of boththe brightfield image and one or more color images of the array, thecolor images being recorded following selection of the desired filtersettings as described above. In one embodiment, color images in the“record Decoding Image” mode are recorded in a “BLUE” and in a “GREEN”channel selected by respective filter combinations (Blue Channel:excitation filter: 405 nm (20 nm); emission filter: 460 nm (50 nm) andbeam splitter: 425 nm (long pass), Green Channel: excitation filter: 480nm (20 nm); emission filter: 510 nm (20 nm) and beam splitter: 495 nm(long pass)) and one color image in the “record Assay Image” mode isrecorded in a “RED” channel selected by a filter combination Red Channel(excitation filter: 640 nm (30 nm); emission filter: 700 nm (75 nm) andbeam splitter: 660 nm (long pass)). The excitation filters transmit onlythose wavelengths of the illumination light that efficiently excite aspecific dye, and an emission filter attenuates all the lighttransmitted by the excitation filter and transmits any fluorescenceemitted by the specimen, and a beam splitter reflects the excitationlight but transmits the emitted fluorescence (the figures in parenthesisindicate the width of the band for each filter). Therefore, the AISsystem, in either “record Decoding Image” mode or “record Assay Image”mode, permits recording of images in two or more color channels.

Processing of images recorded from referenced arrays may be performed byextracting a reference “mesh” or “grid” structure, where individualfields in the grid include beads and the outer dimensions of the gridcorrespond with the dimensions of the referenced array. The principaloperations common to the processing of Decoding Images and Assay Imagesinclude segmentation to locate array boundaries, mesh/grid delineation,image registration (or alignment) and extraction of intensities, aselaborated below. Following completion of processing steps, furtheranalysis is performed by constructing a decoding map from two or moredecoding images using a cluster algorithm and by merging decoding andassay data record to produce decoded assay data record. Partial orcomplete results and related information may be exchanged between two ormore parties, as further elaborated herein.

In the “process Decoding Image” mode, the following operations, as shownin Table III, are utilized. Table III shows the pseudocode for anexemplary processing of a decoding image.

TABLE III LoadImage (BrightField Image); FindBoundary (BrightFieldImage,RotAngle );  /** using brightfield image, find array boundary and angleof misorientation with respect to display edges **/RotateImage(BrightFieldImage, RotAngle); FindGrid (BrightFieldImage,Grid)    /** locus of local intensity minima **/ FOR (each decodingimage be processed) {   LoadImage (Fluorescence Image);   RotateImage(Fluorescence Image, RotAngle);   AlignImage(FluorescenceImage);  /**align fluorescence image   with bright field image **/   OverlayGrid ();   ReadIntensityDistrib(DecodingDataRecord, SampleMesh, Grid,  FluorescenceImage); }These steps are followed by the step of creating a scatter plot from twoor more decoding images and performing a cluster analysis to establish adecoding map. These steps are elaborated below.

In the “process Assay Image” mode, the pseudocode for the processing ofan assay image, as illustrated in Table IV, are used.

TABLE IV LoadImage (BrightField Image); FindBoundary (BrightFieldImage,RotAngle); /** using brightfield image, find array boundary and angle ofmisorientation with respect to display edges **/RotateImage(BrightFieldImage, RotAngle); FindGrid (BrightFieldImage,Grid) /** locus of local intensity minima **/ FOR (each assay image tobe processed) { LoadImage (AssayImage); RotateImage (Assay Image,RotAngle); AlignImage(AssayImage);     /** align assay image with brightfield image**/ OverlayGrid ( ); ReadIntensityDistrib (AssayDataRecord,SampleMesh, Grid, AssayImage); }Histogram expansion is applied to brightfield and color images prior tofinding boundaries, locating grids and aligning images; only theintensity extraction is performed on the 16-bit image as originallyrecorded. The principal operations are implemented using standardmethods (Seul, O'Gorman & Sammon, “Practical Algorithms for ImageAnalysis”, Cambridge University Press) as follows.FindBoundary—Array edges in the brightfield image (and optionally any ofthe color images) are located using a standard Sobel y-gradient operatorimage for left and right edges and a standard Sobel x-gradient operatorfor top and bottom edges. Using these edges, the location of the arrayand its misorientation with respect to the image display boundaries arecomputed. For future use, the array is rotated to bring it intoalignment by with the image display. Prior to edge detection, noise isfiltered by applying six iterations of a morphological “Open” operation,which is an image processing technique. See Seul, O'Gorman& Sammon,supra.Mesh/Grid Construction—A grid or mesh delineating intensity maxima(“peak”) is extracted by tracing the locus of local intensity minima(“valley”) within the brightfield image. This locus defines a mesh orgrid such that each field in the mesh delineates a local intensitymaximum associated with a bead or with a recess provided in patternedsubstrates. In this manner, the grid traces around each of the beads,and includes one bead in each segment of the grid.

To implement the mesh construction, the problem is mapped to Dijkstra's“shortest path” algorithm (see Introduction to Algorithms, T. Cormen, C.Leiserson et al., The MIT Press) well known in the fields ofcomputational geometry and combinatorial optimization, by ascertainingthe intensities of image pixels with values of vertices in a graph. Thealgorithm finds the mesh, also referred to as a grid, as an optimal pathas follows:

-   Pre-process Image:    -   Compute the external gradient image by subtracting a dilated        image from the original image.-   Determine overall orientation, either horizontal or vertical    -   As illustrated in FIG. 15A, provide horizontal and vertical        reference lines of known length to determine the orientation of        the underlying hexagonal grid, compute shortest paths for the        two reference lines and compare to corresponding reference line        lengths by forming. The ratio closes to unity indicates either        horizontal or vertical orientation.-   Find horizontal grid partition:    -   Replicate reference lines by shifting by unit mesh size, then        compute shortest path to determine actual grid line. Continue        until replicated line falls outside array boundary.-   Find vertical grid partition:    -   As illustrated in FIG. 15 b, find the shortest path of a        diagonal line—depending on overall orientation—oriented at        either 30 degrees or 60 degrees with respect to the horizontal        lines in the case of an anticipated underlying hexagonal lattice        of average intensity maxima. Compute intersections of the        diagonal lines and every horizontal line given the intersections        of the diagonal lines and two consecutive horizontal lines,        vertical lines will be located at the midpoint of these        intersections.-   Post-process grid:    -   Grow or shrink grid defined by the totality of the horizontal        and vertical lines to the expected array boundary; correct grid        stagger.-   Store grid:    -   Store grid coordinates in a file.        Image Registration—Given a grid, a registry of the brightfield        image with one or more color images ensures proper alignment by        eliminating possible misorientation and translation (“shifts”)        between the multiple images recorded from a given array. One        source of such shifts is the wavelength-dependent refraction        introduced by standard fluorescence filter combinations.        Registration aligns the assay and decoding images.

Misorientation is eliminated by rotation to bring a given image intoalignment with a reference such as the brightfield image grid. Analternative for aligning images without reference to a brightfield imageis described below.

Assuming only translational displacement, the system can invoke thefollowing fast algorithm. To determine horizontal displacement(“X-shift”), construct intensity profiles along vertical scan lineswithin the array boundary; similarly, to determine vertical displacement(“Y-shift”), construct intensity profiles along horizontal scan lineswithin the array boundary. Next, construct horizontal and verticalprofiles along lines displaced from the first set by one respectivelyone horizontal or one vertical mesh unit. The peak in the profiledetermines the image shift.

The methods herein are limited to shifts between images of less thanhalf of the mesh size, by the optical subsystems of the system includingthe CCD camera used herein. If larger shifts are encountered, imageregistration may be off by one mesh unit in row and/or columndimensions. A Minimal Variance Matching algorithm described below isutilized to correct larger misalignments.

Intensity Extraction. Following completion of image registration, in oneembodiment, intensities are extracted from each color image by samplingthe interior, and not the exterior, of each field of the mesh/grid withan averaging filter mask of suitably chosen size to fit into theinterior of each mesh field. Each intensity value is optionallycorrected by subtracting a supplied background value. One method ofsupplying the background values is to record it as an average of pixelsvalues from an area of the image outside the mesh/grid boundary.

In contrast to widely used conventional methods that invoke peak findingand peak fitting algorithms to locate object positions, the presentmethod offers substantial advantages of processing speed.

Extracted intensities are stored—optionally in binary form—in aone-dimensional array of length L, L denoting the number of units of thegrid/mesh constructed in the course of segmentation. Thisone-dimensional array can be mapped onto the grid or mesh to associateeach intensity value with a unique coordinate within the bead array. Forexample, the following structures may be used:

float intensity[4012];  /** array holding 4012 intensity values **/Struct Grid{     int leftUpX,     int leftUpY,     int rightDownX,    int rightDownY}; /** structure representing one grid field **/ Gridgrid[4012];      /** array holding 4012 grid fields **/Eliminating Reference to Brightfield Image. In certain embodiments, onemay eliminate reference to the brightfield image in the course of imageprocessing, notably during the step of eliminating image misalignment,and indeed to eliminate the step of recording the brightfield imagealtogether. In that case, the step of aligning color images with thedisplay boundaries invokes information extracted directly from the colorimages.

The approach is conceptually as follows. Given a color image, constructhorizontal and vertical intensity profiles by respectively projectingimage intensities to the top-most and left-most scan line in thedisplay, then evaluate the intensity variation in each profile. Next,rotate the color image by a pre-defined angle and repeat the previousconstruction. Continue to rotate until the profiles exhibit maximalvariations, then reduce the step size in rotation angle and reverse thedirection of rotation until the optimal rotation angle is found.

This procedure is significantly improved when information about thearray geometry is available a priori. For example, in one embodiment ofreferenced arrays, a hexagonal geometry with specific choice of nearestneighbor separation, a, and alignment of principal axes with the chipedge is chosen. Then, the desired alignment is characterized by one ofthe profiles assuming the form of a periodic variation with a singleperiodicity, a, and the other profile assuming the form of asuperposition of two phase-shifted periodic functions, both withperiodicity, a*cos 30°. Horizontal and vertical profiles produced bysuch an array at a given misalignment angle thus may be analyzed byfitting each to a superposition of two trial functions and obtaining theangle of misalignment from the fit.

II.1.2 “Non-Referenced” Arrays

Decoding and Assay Images. To perform a multiplexed binding assay inaccordance with the READ™ process, the array is first imaged bymulticolor fluorescence, to determine the color code of constituentbeads which uniquely correspond to the chemical identity of the probedisplayed on the bead surface; second, to record the fluorescenceintensity which indicates the amount of probe-target complex formed oneach bead surface in the course of the binding or hybridization assay.The process of image detection and bead decoding is described inPCT/US01/20179 (WO 01/98765), incorporated herein by reference in itsentirety.Quantitative Analysis. Image analysis algorithms that are useful inanalyzing the data obtained with the READ process disclosed herein maybe used to obtain quantitative data for each bead within an array, asset forth in PCT/US01/20179, incorporated herein by reference. Inpreferred embodiments, data are obtained from the decoding and the assayimages, or preferably from the corresponding decoding image record andassay data record by application of certain algorithms. These algorithmmay be used to obtain quantitative data for each bead within an array.The analysis software automatically locates bead centers using abright-field image of the array as a template, groups beads according totype, assigns quantitative intensities to individual beads, rejects“blemishes” such as those produced by “matrix” materials of irregularshape in serum samples, analyzes background intensity statistics andevaluates the background-corrected mean intensities for all bead typesalong with the corresponding variances. Using calibration beads that areincluded in the assay, intensities are converted to an equivalent numberof bead-bound fluorophores.II.2 Representation and Encryption of Array Configurations—ChipIDII.2.1 Covering. Given a set of probe molecules of types P={p(1), . . ., p(k), . . . , p (n)} and a set of tags, T={t(1), . . . , t(k), . . . ,t(n)}, the former, for example in the form of oligonucleotides ofdefined length and sequence, the latter for example in the form of colorcodes associated with a set of beads, one defines a one-to-one mappingof T onto P whose image represents a covering, C:=C (P) of the set P.The covering is obtained by attaching probes in set P to color-encodedbeads in set T.

In certain embodiments, encrypted coverings serve to conceal theidentity of probe molecules associated with tags by revealing, for eachprobe molecule, only a label or pointer that is logically linked to thatprobe molecule, but not the probe identity itself. This is disclosedonly by a “de-covering” process.

II.2.2 Encoding Random Array Configurations

The random assembly of pooled beads of different types into a planararray creates a specific configuration, thereby defining a “randomencoding”, E, as follows. Given a set of tags, T={t(1), . . . , t(k), .. . , t(n)}, for example in the form of color codes associated with aset of pooled particles, define E as the mapping of T onto a set ofpositions, V={v(1), . . . , v(l), . . . , v(L)}, constructed as follows:from each of n reservoirs of particles, each reservoir containingparticles that are uniquely associated with one tag in accordance withT={t(1), . . . , t(k), . . . , t(n)}, draw r(k) (indistinguishable)particles and place them into r(k) positions randomly selected from aset V={v(1), . . . , v(l), . . . , v(L)}. In a preferred embodiment, Vcorresponds to the vertices of a rectangular array, {(i, j); i=1, . . ., I, j=1, . . . , J} or, equivalently, {l; l=1, . . . , L:=I*J}, ofdesignated positions (“traps”) in a silicon substrate.

In certain embodiments, encoding serves as a further level of encryptionto conceal the identity of tags which is revealed only by the decodingprocess. In addition, standard encryption techniques may be applied tofurther conceal encoding and covering information. Decoding of the arrayconfiguration identifies the tag assigned to each of the positionswithin V. For example, each such color code, identified by a unique tagindex, may be obtained by combining fluorescent dyes of fundamentalcolors, R, G and B, for example, in specified ratios to produce beadsproducing fluorescence signals of intensities (I_(R), I_(G), I_(B)), forexample, in the respective color channels, R, G and B. The systemdescribed herein can maintain this information in a separateconfiguration file, which is generated in conjunction with theproduction of bead libraries.

Representations. In one embodiment, encoding is achieved by creatingspectrally distinguishable particles by way of staining them with two ormore dyes in accordance with one of several possible possibilities. Forexample, several fluorophore tags in the form of dyes, Red (R), Green(G) and Blue (B), for example, may be combined in a variety of fixedR-G-B molar ratios or may be combined in binary (or other) fashion, eachdye being either present or not present in any given particle type.Decoding of an array of color-encoded particles is performed asdescribed herein by recording a set of images showing fluorescenceintensities in separate color channels for each of the fundamental dyesand determining molar ratios by analyzing intensities in the variouscolor channels.

The information from multiple decoding channels may be represented in amerged decoding intensity array which forms part of the Decoding DataRecord described herein, by listing, for each position v(l), 1 . . . l .. . L, a set of intensities (I_(R), I_(G), I_(B))_(l), for example, orlisting relative abundances that are obtained by normalizing intensitiesby suitable internal standards. Optionally, to obtain a compact integerrepresentation, intensities may be represented in binary form, I=2^(p),0≦p≦16 so that a set of exponents (P_(R), P_(G), P_(B))_(l) may bestored for each position.

Further analysis, performed as described herein in susbsequent sections,serves to construct a decoding map. This map is composed of clusters,each cluster representing one spectrally distinguishable particle typewhich in turn is defined by a triplet of fundamental tags, such as(I_(R), I_(G), I_(B)). Once the decoding is in hand, clusters may begiven a simple index which now serves as a tag index. That is, thetriplet is replaced by a simple tag index. Accordingly, the randomencoded configuration generated by E may be represented in the form ofthe random sequence of L=Σr(k) tag indices assigned by the encoding E topositions (v(1), . . . , v(l), . . . , v(L)). In certain embodiments, itwill be convenient and useful to sort this sequential representation bytag index so as to obtain a one dimensional array of n lists, the k-thsuch list containing the sequence of r(k) array positions occupied bytag k. If the positions are identified by the corresponding vertex arrayindex, this provides a particularly compact representation.

Alternatively, a representation in the form of a 1-d array of length Lof tag indices also may be convenient. For example, the configuration ofan array composed of 4,096 or 212 beads of 128=27 types, could be storedin 4k*2 Bytes=8 kB of non-volatile memory which could be packaged withthe carrier.

Individual Array Configurations: IntrinsicChipID. In a preferredembodiment of a random encoded array assembled on a silicon chipsubstrate, the array configuration, in any of the aforementionedrepresentations, provides an identifying tag for the substrate. See U.S.patent application Ser. No. 10/365,993 “Encoded Random Arrays andMatrices.” filed Feb. 13, 2003, incorporated by reference. Each suchInstrinsicChipID is drawn from the number, S, of distinguishableconfigurations of a random encoded array of I*J=L vertices, given by thenumber of ways in which n (unordered) samples of r(k)(indistinguishable) particles of type t(k), 1≦k≦n, may be distributedamong L positions:S(L; n; r(k), 1≦k≦n):=L!/r(1)!r(2)! . . . r(k)! . . . r(n)!Illustrating the large number of possible combinations is the fact thatan array of L=16 positions, composed of n=4 bead types, where each typeis represented four times (r(1)= . . . r(4)=4), can display S(16; 4;r(k)=4; 1≦k≦4)=16!/(4!)^4, or approximately 63 million configurations.The InstrinsicChipID may be cast in any of the representations discussedabove.Degree of Randomness: Autocorrelation Function of Tag Sequence. Indeed,the degree of randomness of a given bead array is readily ascertained byconstructing the autocorrelation of the tag sequence corresponding tothe random configuration of encoded beads within the array as elaboratedabove. For example, a random sequence of length 22 and composed of threetags, R, G, B with relative abundance 7/22=⅓, will produce anautocorrelation function, g, of this type with the following behaviornear the origin:

Shift: −1 R G G B R G R R B R G R B R G B B R G G B G g = 3 R G G B R GR R B R G R B R G B B R G G B G Shift:  0 R G G B R G R R B R G R B R GB B R G G B G g = 22 R G G B R G R R B R G R B R G B B R G G B G Shift:+1 R G G B R G R R B R G R B R G B B R G G B G g = 3 R G G B R G R R B RG R B R G B B R G G B GScoring each tag match in the autocorrelation as 1, each tag mismatch as0, it is readily seen that the (normalized) autocorrelation function ofthe random tag sequence will exhibit a sharp peak and will drop—within asingle unit shift—to the average value of ˜(1/r)^2, r denoting theaverage redundancy of each tag. This property of random encoded arrayswill serve to construct a robust “matching by variance minimization”algorithm to combine decoding and assay data records, as describedherein.II.3 Image Analysis

Completion of the aforementioned image processing steps yields a compactrepresentation of the intensity distributions in decoding and assayimages which facilitates further analysis. This analysis includes thesteps of generating a decoding map from the set of decoding images andcombining (“merging”) decoding and assay images to generate final assayresults using a matching algorithm further elaborated below.

II.3.1 Construction of Decoding Map by Cluster Analysis

A decoding map assigns each bead located in the processing of a decodingimage to a unique group in accordance with its unique tag. For example,color-encoded beads will be grouped by color and/or by intensity of eachof two or more encoding colors, as assumed here for clarity in theexposition of the clustering algorithm. It will be apparent that othercodes are possible here and will be used in analogous fashion. Thesystem herein may include two or more clustering algorithms.

II.3.1.1 Matching to Map Template

This algorithm anticipates a decoding map template, constructed manuallyor otherwise provided, which provides seed locations, each anticipatedgroup or cluster in the decoding map corresponding to one such seed.This is particularly advantageous in the situations commonly encounteredin practice involving analysis of a decoding image recorded from beadarrays of the same batch or lot. That is, the number of anticipatedclusters, and their respective approximate central locations, are knowna priori. Assuming, for purposes of illustration, ratio encoding by twoencoding colors, the algorithm produces a partition of a given scatterplot of decoding intensities which is first converted into atwo-dimensional histogram.

The map template matching algorithm first generates a two-dimensionalhistogram image of the input data, optionally providing smoothing to thehistogram image to eliminate noise. Given the two-dimensional histogram,the decoding group is generated using a “watershed” algorithm, wellknown in the art in connection with image segmentation, which treats theintensity histogram as a topographical map showing local elevation asfunction of position. Starting at the lowest point, the “water level” isnow gradually increased until “water” starts to spread over twopreviously separate compartments. A “dam” is constructed at the“overflow” position. The set of dams so constructed represents the setof segment boundaries.

To implement these steps of generating the decoding map, the maptemplate matching algorithm uses three auxiliary objects: a priorityqueue, a stage (of processing) image and a label image. The priorityqueue maintains individual pixels in accordance with their intensityvalues as obtained from the two-dimensional histogram of the inputscatter plot, keeping the pixel with the maximal value at the top. Thestage image serves to track the stage of pixel assignment: any givenpixel either is or is not assigned to a group or cluster. The labelimage serves to track the group identity of each assigned pixel.

The algorithm proceeds as follows. For each given seed, initialize thecorresponding pixel in the label image by assigning it the seed label,add its eight nearest neighbors to the priority queue and mark each ofthese pixels “assigned” in the stage image. Next, pop the top pixel fromthe queue and inspect its eight nearest neighbors, ignoring unassignedpixels and checking whether all “assigned” neighbors have the samelabel. If so, mark the pixel with that label, otherwise, leave the pixelunmarked. Finally, add to the queue all non-zero neighbors not currentlyin the queue and pop a new element. Continue until queue is empty—atwhich point the label image shows the group assignment for each pixel.

The resulting partition assigns each data point in the scatter plot toone and only one of the groups (“clusters”) identified by the set ofgiven seed locations. Two or more of such sscatter plots are processedif three (or more) colors are used for encoding. The algorithm performsthe operations as illustrated in Table V. Table V shows the pseudocodefor the operation of constructing a Decoding Map by way of templatematching.

TABLE V CreateScatterPlot(Image, IntensityArray(FirstColor),IntensityArray(SecondColor));       /** 2d Plot of Intensities extractedfrom       Decoding Images in       different Selected color channels**/ ConvertScatterPlotToTwoDimHistogram(Image); SmoothImage(Image,Kernel);  /** apply 5*5 Smoothing Filter **/SetMapTemplate(MapTemplate); /** provide map template **/GetSeedLocations(MapTemplate, ClusterSeeds);GenerateDecodingMap(ClusterSeeds);II.3.1.2 Fast Clustering Algorithm

The system described herein also includes a fast algorithm that invokesgraph theory to construct a two-dimensional decoding map without the aidof a template. The algorithm converts the input scatter plot ofintensities into a “distance graph,” each data point in the scatter plotrepresenting one node in the graph, and each such node being connectedby one edge to its K nearest neighbors (by Euclidean distance). Eachedge is assigned a weight that is proportional to its length, and eachnode is given a value computed from the weight of the largest edgeconnected to that node.

The algorithm comprises the following steps. First, load scatter plotand convert it to a distance graph image. Process the graph image byapplying a morphological Open operation to each connected graph—thesteps of erosion and dilation constituting the open operation will alternode values. Next, for each node in turn, eliminate all edges whoseweight exceeds the node's new value. Then, partition the graph intoconnected components, a connected component or cluster being defined asa sub-graph of connected nodes—each node within a connected subgraph canbe visited by traversing edges. Finally, filter out small groups andsplit large groups into two groups if necessary. The algorithm thusperforms the following steps, as illustrated in Table VI. Table VI showsthe pseudocode for the operation of constructing a Decoding Map by fastcluster analysis.

TABLE VI CreateScatterPlot(Image, IntensityArray(FirstColor),IntensityArray(SecondColor)); ConstructDistanceGraph(GraphImage, Image);/** assign value to each node **/ Open(GraphImage); For each(Node in theGraphImage){ /** process all nodes in the graph **/    For each(Edge ofthe Node){ /** process all edges of a node **/      If( Edge > Node){       DeleteEdge( );      }    } } PartitionGraph(GraghImage); /**partition graph into connected subgraphs (“clusters”) **/PostProcess(GraphImage);Three-Color Encoded Objects: “Stacked” Decoding Maps. The clusteringalgorithm is applied to handle multi-dimensional cluster analysis forpopulations constructed as stacked two-dimensional clusters by precedingthe clustering operation with a sorting step. In stacked two-dimensionalclusters the third decoding image acquired in a case of encoding bythree-color combinations will have one or more discrete intensitylevels. Considering first the case of just a single intensity level,particles are readily sorted into two groups, namely those containingthe third dye (labeled ON) and those not containing the third dye(labeled OFF) to obtain a stack of two-dimensional scatter plots whichare individually analyzed to generate two corresponding decoding maps.In practice, this operation is performed using the DECODER as follows.First, generate a two-dimensional scatter plot for the original twodyes, encoding colors, designated G and B. This “G-B” plot represents anintermediate result that corresponds to the projection of thethree-dimensional “R-G-B” space onto the “G-B” plane. To split thisprojected scatter plot into its constituent components, generate atwo-dimensional scatter plot just for third color, designated R, in the“R-R” plane by providing two copies of the decoding image recorded inthe R-channel. The “R-R” plot will have the same size (and number ofeventual clusters) as the “G-B” plot (containing, after all, images ofthe very same objects). The “G-B” plot is now split into two plots, onecontaining only points corresponding to “R-OFF”, the other containingonly points corresponding to “R-ON”. A (two-dimensional) decoding map isnow constructed for each plot using one of the algorithms describedabove. This strategy is readily generalized to populations encoded usingmultiple levels of a third color.II.3.2 Combining Assay and Decoding Images

The identity of the binding agent of the binding agent-analyte complexis determined by decoding. This step entails comparison of decodingimage(s) and assay image(s). That is, the assay image is sampled inaccordance with the cluster information in the decoding map to groupassay signals by bead type and hence by encoding tag as described above.A robust matching algorithm ensuring alignment of decoding images andassay images is described below.

Decoding may be carried out at the user site or at a central location.For example, decoding images—in a suitable representation such as mergeddecoding intensity arrays—are made available to the user, either in theform of text files on a recording medium that is distributed along withbead arrays or in the form of a downloadable file available by way ofauthenticated access to a central database. Alternatively, decoding maybe carried out on a central server after uploading of the assay imagefrom the user site. Transaction protocols for this and related mode ofdata communication are disclosed below.

Matching by Variance Minimization (MVM). By construction, constituentbeads of a randomly encoded bead array are randomly dispersed over thearray. Assay signals recorded from a set of beads randomly drawn fromall subpopulations or types of beads, these beads displaying differenttypes of probes, will exhibit an inter-population variance, V, thatreflects the differences in the corresponding probe-target molecularinteractions. Assuming equal abundance of all bead types, thisinter-population variance may be approximated by the variance associatedwith the distribution of the mean assay signals evaluated over eachsubpopulation, namely:

${V = {\sum\limits_{j}^{\;}\left( {{\operatorname{<<}I}\operatorname{>>}{- {< I >_{j}}}} \right)^{2}}},$<I>_(j) denoting the mean assay signal of the j-th subpopulation and<<I>> denoting the mean of the <I>_(j). In contrast, assay signalsrecorded from a set of beads drawn from the same subpopulation, thesebeads displaying the same type of probe, will exhibit anintra-subpopulation variance, v, that reflects aspects of characteristicremaining chemical heterogeneities such as bead size, density of probesdisplayed on beads, assay binding efficiency, etc, given that allprobe-target interactions within the subpopulation are nominallyidentical for each subpopulation; the intra-population variance has theform:

${v = {\sum\limits_{k}^{\;}\left( {< I > {- I_{k}}} \right)^{2}}},$I_(k) denoting the assay signal recorded from the k-th bead within thesubpopulation. Except in special circumstances, assay signals recordedfrom different subpopulations will be uncorrelated, and the variance, V,will exceed v:V>>v

It is this insight which forms the basis for a robust “matching byvariance minimization” algorithm by which to perform the crosscorrelation of decoding and assay images recorded from a random arrayand to resolve the task of perfectly aligning the two (or more) imagesof interest in the absence of fixed alignment aides and in the presenceof edge-corrupting noise. That is, only in the correct alignment ofdecoding image and assay image are assay signals within the assay imagesampled over members of the same subpopulation. In one embodiment, thealignment is performed by monitoring the variance, computed for one ormore specific subpopulations of beads that may be included in the arrayfor this purpose, as the assay image is shifted with respect to thedecoding image.

As discussed herein in previous section II.2.2, even a single stepdisplacement will completely scramble the sampling and mix assay signalsfrom multiple subpopulations. Accordingly, the correct alignment, evenin the presence of considerable edge corruption, is robustly indicatedby minimizing the variance of assay signals over a subpopulation as afunction of relative displacement. In practice, “dark” particles orobjects are filtered out during this matching step to eliminateerroneous contributions to the variance. The FilterDarkBeads function isrequired to eliminate from the assay image objects or microparticleswhich do not contribute a measurable signal because—while the center ofbright objects coincides with the maximum in the intensity profile itcoincides with the minimum in the intensity profile for dark objects.This can lead to errors in aligning assays and decoding images. Inpractice, each array is designed to include one or more referencesubpopulations displaying positive or negative control probes which aredesigned to generate a signal of known magnitude so as to ensure thatindeed v<<V. These one or more reference subpopulations are sampled tominimize the corresponding subpopulation variances, v, as a function ofdisplacement from perfect alignment. That is, v is evaluated as theassay image grid is shifted relative to the decoding image grid. If thecondition v<<V is not satisfied, the algorithm produces a warning toindicate a possible problem with image quality and provides a choice toabandon further analysis.

As illustrated in FIGS. 9A and 9B, there are seven possible misalignedpositions if the search is confined to single row and single columnmisalignments, namely: (−1,−1), (−1, 1), (−1, 0), (0, 0), (1, 0), (−1,1), (1, 1); that is, to account for the stagger introduced in the imagegrid derived from a hexagonal lattice (910, 920), seven distinctpositions are checked, as illustrated in FIG. 9C.

The Minimal Variance Matching algorithm operates on Decoding and AssayData Records and evaluates the desired variance as a function of unitshifts applied to the two images represented in the respective datarecords. Table VII shows the pseudocode for the operation of combiningdecoding and assay images by way of Matching by Variance Minimization.

TABLE VII LoadDecodingData(DecodingDataRecord, DecodingMap);LoadAssayData(AssayDataRecord, IntensityArray); MinVariance = −1000;MinVarianceLocation = 0; /** Check Variance Produced by 7 Possible UnitDisplacements - see Text **/ For (i=0; i< 7; i++){  ShiftGridPosition(i);   FilterDarkBeads( );   Variance[i] =Merge(DecodingMap, IntensityArray);   IF(Variance[i] < MinVariance){    MinVariance = Variance[i];     MinVarianceLocation = i;   } };WriteAssayData(MinVarianceLocation, AssayDataRecord);  /** Save Location**/Correction for Multiple Scattering Effects. Unless suspended inspecially selected density matching fluids which generally will beincompatible with bioanalytical assays of interest, polymeric, ceramicor other microparticles will exhibit strong scattering of visible lightso that multiple scattering effects are readily observed in planarassemblies and arrays of such particles. For example, fluorescenceemitted by a microparticle within such a close-packed planar array isdiffracted by its nearest neighbors and possibly its more distantneighbors, a source of potential error in overestimating assay signals.This effect is strongly dependent on interparticle distance and may bediminished or eliminated by appropriate array and substrate design aswell as choice of illumination and collection optics.

In addition, the system herein also offers a method of correcting forthe effects of multiple scattering on the intensity distributionrecorded from subpopulations of beads within a planar array. As with theMVM algorithm, this method takes advantage of the random spatialdistribution of different bead types throughput the array. Randomlyplaced “blank” beads, drawn from a subpopulation that is at most weaklyfluorescent for purposes of encoding, serve as local “antennae” toestablish a random sample of excess fluorescence produced by way ofdiffraction by the nearest neighbor configurations encountered withinthe array. To correct for the principal global effect of multiplescattering on assay signals recorded from a random encoded array, thevariance of this excess fluorescence signal is subtracted from theintra-population variance of all subpopulations.

III Transmission of Image Data Records

III.1 Identifying and Tracking Bead Arrays

As described in U.S. patent application Ser. Nos. 10/238,439, and10/365,993, entitled: “Encoded Random Arrays and Matrices” (thespecification corresponding to WO 01/20593), incorporated herein byreference, and as further elaborated herein in Sections II.2.1 andII.2.2., each bead array generates its own unique ID (“Intrinsic ChipID”or “ChipID”) and can be identified. This ChipID may be physically orlogically linked to a CarrierID. For example, multiple bead arrays maybe mounted on a multichip carrier comprising a bar code which is capableof recording the identity of each of the bead arrays. The CarrierID istracked in the course of producing bead arrays and also is tracked inthe course of acquiring, processing and analyzing assay images using thesystem of the invention. The IntrinsicChipID may be linked to aCarrierID, and to a further assigned ChipID which may be appended to theCarrierID (“Appended ChipID”) or may be otherwise physically linked tothe CarrierID. Unless specifically indicated below, the ChipID shall beunderstood to refer to Intrinsic ChipID or Appended ChipID.

Samples of interest for chemical analysis including samples collectedfrom patients for clinical or other testing, also may be given a sampleor patient ID, e.g., in the form of a barcode which may be tracked alongwith the CarrierID using a barcode scanner. In addition, methods ofintra-analyte molecular labeling have been previously disclosed. Forthese purposes, the addition of unique molecular external labels orinternal labels, for example in the form of a DNA fingerprints, may beconsidered. Such labels and associated methods have been described inU.S. patent Ser. No. 10/238,439. Information derived from theexamination of these molecular labels may be entered into decodingand/or assay image records by the system herein to minimize samplehandling error and to facilitate the secure exchange of information, aselaborated herein below.

III.2 Transmission Protocols

In one embodiment, an assay image may be submitted for analysis bytransmission over a network connection to a central location. Softwareavailable on centrally located servers, in one embodiment involving theANALYZER described herein, completes the analysis and makes results ofthe analysis available to authorized users for retrieval. Theanalysis-server model provides protocols governing exchange ofinformation in one or more two party transactions between one or moreusers and a central server where data is analyzed (FIG. 10 a), andfurther provides protocols governing the exchange of information inthree-party transactions, involving patient, to provider to a testingcenter where data is analyzed and assay results are recorded (FIG. 10b). A three-party transaction may also involve a recipient, a mediatorand a provider of information.

This model offers several advantages to users as well as suppliers ofmolecular diagnostics, particularly when instrumentation distributed tofield locations is easy to use and maintain, while the analysis of thedata obtained using the instruments is complex. To the users' benefit,the requirement for designated staff with the training and expertise toinstall, master and operate analytical software is eliminated. Rapidturn-around of even advanced data analysis is ensured by access to rapidnetwork connections even in remote locations at a doctor's office orpatient site. Suppliers benefit from the reduction in cost associatedwith the logistics of providing extensive technical software supportwhile providing high-speed analysis on dedicated server hardware. Ingeneral, the analysis service provider, equipment manufacturer and assaydeveloper all may be distinct parties. Additional parties mayparticipate in certain transactions. For example, the manufacturer ofthe chips and arrays, and the analysis service provider, may not be thesame party.

The system herein provides for an analysis server model invokingtransaction protocols such as those elaborated below that ensure thesecure exchange of private information created, for example, in geneticanalysis—an issue of wide concern. Additional services, such as advancedanalysis in the form of binding pattern matching via database searchesor data archiving, are readily integrated. In one embodiment, theanalysis server model applies to assays producing data in the form ofimages and the analysis of interest relates to the analysis andarchiving of images.

In one embodiment of such a transaction, the exchange of information mayrelate to the completion of analytical chemical, biochemical anddiagnostic test, and the participating recipient, intermediary andprovider of the information of interest (e.g., personal geneticinformation) may correspond to patient, testing center and (data)analysis provider, respectively. Protocols are set forth for the securecreation, exchange and storage of information

Transaction Protocols

Pattern Matching via Access to “Fingerprint” Database. A pharmaceuticalcompany researcher (“CLIENT”) submits data recorded from an assayperformed in an array format to probe the interaction between a set ofimmobilized proteins (“receptors”) and a second set of proteins orligands provided in a solution that is brought in contact with thereceptor array. The data may be in the form of a decoded assay datarecord as described herein. Intensities—recorded from bead arrays orfrom other arrays, including “spotted” arrays—reflect a certain patternof interactions between receptors and ligands. Alternatively, the datamay represent a pattern of expression levels for a set of designatedgenes of interest that may indicate an individual patient's response totreatment or may indicate a toxicology profile or may indicate theresponse triggered by a compound of interest that is to mimic the actionof a known drug, said action being characterized on a molecular level bysaid expression pattern.

The two-party transaction between PROVIDER and CLIENT is performed inaccordance with a protocol that preserves the anonymity of the CLIENTand simply permits the CLIENT to search a PROVIDER database for matchingthe interaction pattern or expression pattern with a unique pattern (or“fingerprint”).

Advanced Services. Once decoded assay results are in hand, additionalanalysis may be optionally performed. In the simplest instance,statistical measures such as mean or variance are readily evaluated overeach of the subsets. More generally, the presence of characteristic“patterns” of receptor-ligand interaction may be ascertained Suchpatterns may be indexed and stored in a searchable database to providethe basis for assay interpretation. This in turn will facilitatetracking and interpretation of disease histories and clinical trialresults and aid in the identification of molecular identifiers andfeatures (“genotype”) associated with clinical pathology (“phenotype”).

File Serving: Authenticated Remote Access to Decoding Image.—Decryptionof the message contained within the assay image by application of thekey represented by the decoding image groups intensities in accordancewith bead type is provided. Following bead array assembly, the decodingimage is analyzed to derive a temporary ChipID representing a portion ofthe complete ChipID, based, for example, on the first row or column ofthe decoding image. The ChipID is stored, the temporary ChipID istransmitted to the user as a password for access to the database andretrieval of the full decoding image. In certain situations, it may beadvantageous to the user not to download the full decoding image. Forexample, if assay results are negative, conclusions about a set oftested receptor-ligand interactions may be reached without decryption.

Alternatively, assay images may be uploaded to the server for additionalanalysis or archiving. Incoming assay images are linked to storeddecoding images by matching temporary and full ChipID codes.

In this file server model, fees may be charged in accordance with thevolume of transactions on a single transaction basis or on asubscription basis in accordance with pricing models practiced in theapplication server market.

A Two-Party Transaction Relating to the Analysis of Encrypted Assay Data

The following two-party transaction (FIG. 10 a) illustrated here usingan embodiment in the form of custom bead arrays, invokes an encryptedcovering to ensure that the identity of compounds in the assay remainprivate.

For example, a pharmaceutical company researcher (“CLIENT”) provides tothe custom bead array provider who, in this example, also is theanalysis service provider (“PROVIDER”), a library of compounds to besubjected to on-chip assays in labeled containers with instructions tocreate an encrypted covering by simply recording the container labelscorresponding to specific bead types. For example, “compound incontainer labeled A anchored to bead tag T1.” The identity of compoundswithin labeled containers is known only to CLIENT. In a preferredembodiment, a unique set of bead tags is selected for a specific clientto minimize mishandling or inadvertent swapping of compound libraries.

The two-party transaction between PROVIDER and CLIENT is performed inaccordance with the following protocol:

Provider—Provide Bead Array with Unique ChipID

-   Create encrypted covering by attaching designated compounds to    “tagged” beads-   Create array encoding by assembling pooled beads into an array-   Decode array configuration-   Establish ChipID. In a preferred embodiment, the ChipID is derived    from the array configuration-   Optionally, store ChipID in non-volatile memory to be packaged along    with bead array chip-   Create a public database record (“Key”) of the form (ChipID,    Encrypted Covering)-   Send bead array chip in assay cartridge (with ChipID) to Client    CLIENT—Perform Assay and Transmit Assay Image-   Receive assay cartridge from Provider-   Place analyte solution into assay cartridge and perform assay-   Record ChipID. In one embodiment, use a chip carrier containing    ChipID in electronic representation in conjunction with an    electronic reader and the array imaging system such that the ChipID,    read out from the assay chip, is recorded and stored, and thus    unambiguously linked, with the encrypted message in a public record    (ChipID/Public, Encrypted Message/Public)-   Record assay image and create assay data record (“Encrypted    Message”)-   Send combination of ChipID and Encrypted Message to PROVIDER for    analysis    Provider—Perform Image/Data Analysis-   Receive public record (ChipID, Encrypted Message) from CLIENT-   Strip ChipID and check database for matching record (ChipID,    Encrypted Covering)-   Use ChipID to decode message, thereby creating a profile. In one    embodiment, the profile representing the decoded message has the    form {<I>_(k), 1≦k<≦n}, where the <I>_(k) represent intensities    averaged over all beads of tag type k, tags being uniquely    associated with a specific compound in accordance with the encrypted    covering-   Create updated database record (ChipID, Encrypted Covering, Profile)    Provider—Transmit Profile-   Supply database record (ChipID, Encrypted Covering, Profile) for    retrieval by CLIENT.    A Three-Party Transaction for the Secure Exchange of Genetic    Information. By generating, either concurrently with the completion    of genetic analysis or by concurrent analysis of tagging molecules    added to patient samples, a molecular ID such as a DNA fingerprint    (as described in U.S. patent application Ser. No. 10/238,439) that    is embedded within the assay image, the methods of the present    invention create an unambiguous link between a chip ID    (“IntrinsicChipID”) derived from the configuration of a random    encoded bead array and a unique genetic ID, thereby not only    minimizing the possibility of error in sample handling but also    enabling verification of assay results and securing confidential    genetic information in the course of two-party or multi-party    transactions, as elaborated below for a three-party transaction.

The process illustrated in FIG. 10C using a preferred embodiment in theform of custom bead arrays packaged on a carrier within a fluidic assaycartridge ensures the confidentiality of personal genetic information.Specifically, a three-party transaction between custom bead arrayprovider who, in this example, also is the analysis service provider(“PROVIDER”), an intermediary or facilitator such as an assay serviceprovider (“TESTTING CENTER”) and user (“PATIENT”) may be organized asfollows to ensure privacy of information generated by genetic testing.

In this protocol, transactions between PROVIDER and TESTING CENTER andbetween TESTING CENTER and PATIENT involve public records that areidentified by the ChipID. A separate transaction between PROVIDER andPATIENT involves the private information in the form of the geneticprofile. The protocol below ensures that the identity of the PATIENT isconcealed from PROVIDER: the PATIENT is identified only by the geneticID presented for authentication in the final retrieval of geneticinformation. On the other hand, in the protocol below, geneticinformation is made available—by way of a “Relay” step—to the TESTINGCENTER—or a designated physician or genetic counselor—for communicationto the PATIENT.

The three-party transaction (FIG. 10C) between PROVIDER, TESTING CENTERand PATIENT is carried out in accordance with the following protocol.

Provider—Provide Encoded Bead Array Chip with Unique ChipID

-   Create covering by attaching probe molecules to color-encoded beads-   Create array encoding by assembling pooled beads into an array-   Decode configuration to establish ChipID-   Optionally, store ChipID in non-volatile memory to be packaged along    with bead array chip-   Create a database record (“Key”) of the form (ChipID/Public,    Covering/Private)

Send packaged chip (with ChipID) to TESTING CENTER

PATIENT—Request Analysis

-   Submit sample to TESTING CENTER-   Receive ChipID from TESTING CENTER    TESTING CENTER—Perform Assay-   Receive assay cartridge from PROVIDER-   Collect sample from PATIENT into assay cartridge-   Send ChipID to PATIENT-   Complete sample preparation and perform genetic analysis    TESTING CENTER—Record Assay Image and Transmit Assay Data Record-   Record assay image with embedded GeneticID (“Encrypted Message”)-   Send combination of ChipID and Encrypted Message to PROVIDER for    analysis. In a preferred embodiment, use a chip carrier containing    ChipID in electronic representation in conjunction with an    electronic reader and an image acquisition system under general    processor control such that the ChipID, read out from the assay    chip, is recorded and stored, and thus unambiguously linked, with    the encrypted message in a public record (ChipID/Public, Encrypted    Message/Public)-   For later verification: store assay cartridge, optionally containing    patient blood sample    Provider—Perform Image/Data Analysis-   Receive public record (ChipID/Public, Encrypted Message/Public) from    TESTING CENTER-   Strip ChipID and check database for matching decoding data record    (ChipID/Public, Covering/Private)-   Use covering to fully decode message identifying genetic profile and    embedded GeneticID    -   In one embodiment, the decoded message has the form of        intensities, {<I>_(k), 1≦k≦n}, averaged over beads of the same        type, each type uniquely identifying a specific probe molecule;        other representations also are available as discussed herein.-   Extract GeneticID from decoded assay data record image as the subset    of intensities <I>_(k) corresponding to ID-specific probes-   Create updated database record (ChipID/Public, Covering/Private,    GenID/Public, GenProfile/Private)    Provider—Transmit Genetic ID-   Send (ChipID/Public, GenID/Public) to Testing Center for    transmission to Patient    PATIENT—Receive Genetic ID and Retrieve Genetic Profile (1080)-   Using (ChipID/Public, GenID/Public), query Provider database    Provider—Transmit Genetic Profile (1090)-   Authenticate GenID to authorize retrieval of private genetic profile    from database If, and only if, authentication confirmed, retrieve    (ChipID/Public, Covering/Private, GenID/Public, GenProfile/Private)-   Supply database record (GenID/Public, GenProfile/Private) for    retrieval by PATIENT

Using an assay cartridge, a physical linkage can be created betweenpatient sample, assay cartridge and BeadChip with associated ChipIDwhile the embedded genetic ID creates a physical linkage between geneticidentity and genetic profile as an inherent part of the assay.Verification is then always possible by retesting. The physical andlogical linkages created by the methods of the present invention betweenpatient sample, ChipID and genetic profile with embedded genetic IDeliminate common sources of error in genetic testing such as switchingof patient samples.

Other transaction protocols may be devised using data structures of thetype introduced in the foregoing example, to ensure that only thePATIENT has access to genetic (or other) information created in an assayperformed at the TESTING CENTER. For example, in one embodiment of thepresent invention, the PATIENT already may be in possession of his/herGeneticID prior to initiating a three-party transaction. In that case,the steps of transmitting, relaying and receiving GeneticID (FIG. 10 c,1050, 1060, and 1070) may be eliminated. Instead, the PATIENT directlyrequests transmission of the genetic profile from the PROVIDER—access tothe relevant database may be authenticated by comparing the Genetic IDused in the request with the Genetic ID extracted from the geneticprofile.

Decoded assay data records may be archived. Archived decoded assay datarecords would be accessed only by those in possession of a GeneticID orequivalent key embedded in the decoded assay data record. That is, thedatabase of archived records would be searched by rapidcross-correlation with the authentication code.

More generally, the following three-party protocol ensures that only thePATIENT (or his/her designee, such as a physician) who initiates atesting procedure is in possession of private information created in thetest performed at the TESTING CENTER and analyzed by the applicationservice PROVIDER. The TESTING CENTER has no access to the privateinformation and PROVIDER has no knowledge of the identity or particularsof the PATIENT. The PATIENT, having requested and having been assigned,a ChipID and SampleID, requests, directly from the PROVIDER, aconfidential authentication key. In one embodiment, this is accomplishedby access to the PROVIDER site, for example by a remote login. If theconfidential authentication key generated by the combination of ChipIDand SampleID is taken by a third party, the PATIENT will have immediateknowledge that the confidential authentication key may be at risk ofdisclosure to a third party. The PATIENT may be able to request a newSampleID before providing a new biological sample to the TESTING CENTER.Software then assigns a randomly selected encrypted personalizedauthentication key to the combination of ChipID/SampleID presented inthe request. Only one such assignment is permitted. In one embodiment,the encrypted authentication key has the form of a “cookie” that isplaced in a hidden directory on the hard drive or other storage deviceof the requesting machine so that only that machine is authenticated forfuture retrieval of testing data from the PROVIDER. The PATIENT willensure the integrity of the process: should an unauthorized party, forexample, at the TESTING CENTER, attempt to acquire an authenticationkey, the subsequent attempt by the PATIENT to do so would fail, alarmingthe PATIENT to a possible breach in protocol. In one embodiment, theencrypted authentication key assigned to the requesting ChipID/Sample IDcombination will be the IntrinsicChipID, or information embeddedtherein. In one embodiment, the random string of integers indicatingvertex positions of a designated specific bead type within the BeadChipmay be used. Following submission by the TESTING CENTER of the AssayData Record, identified by a ChipID, for analysis, the PROVIDER combinesthe Assay Data Record with the Decoding Data Record corresponding to thesubmitted ChipID so as to create a decoded Assay Data Record from whichspecific embedded information such as a genetic profile may be extractedby “De-Covering”, that is, application of the Covering to identifyspecific probes within the array as previously elaborated herein. Thisinformation is made available for retrieval by the PATIENT using theencrypted authentication key previously assigned to the ChipID/Sample IDcombination. In one embodiment, only the machine previously endowed witha “cookie” will be permitted to access the database containing therequested information. This protocol ensures that the TESTING CENTERknows only the identity of the PATIENT but not the information such as agenetic profile extracted from the assay while the PROVIDER knows theinformation such as a genetic profile but not the identity of thePATIENT.

It will be apparent to those skilled in the art that the foregoingspecific instances of two-party and three-party transactions merelyillustrate the concepts involved which are applicable to a wider rangeof applications.

Pricing Strategies. The analysis server model of the present inventionprovides “fee-for-service”—in a single transaction format or insubscription pricing format—in which the initial cost of instrumentationas well as the recurring cost for disposable items can be absorbed inthe charges for one or more of a palette of services. This has theadvantage of eliminating user capital expenditures. The charges are foranalysis, not for enabling instrumentation or assay components.

EXAMPLES Example 1 Acquiring and Processing Decoding Image(s)

FIG. 11 illustrates the processing steps performed by the DECODER.Displayed in a Graphical User Interface (GUI) (1100) are three images,namely: a Brightfield image (1110), a green fluorescence image (1120)and a blue fluorescence image (1130). Grids, extracted and aligned bythe DECODER, also are displayed. Also displayed is a scatter plot (1140)produced by the DECODER from the intensities in green and bluefluorescence images.

Example 2 Constructing Decoding Map

FIG. 12 illustrates the construction of the 2D decoding map by theANALYZER which may be integrated with the DECODER whose GUI is shown(1200). The map of the present example is composed of 33 clusters(1210), each of which is assigned a unique tag index. This is displayedfor each cluster along with the number of beads contained in thatcluster. For example, cluster 1 contains 101 beads.

Example 3 Acquiring and Processing Assay Image(s)

FIG. 13 illustrates the processing steps performed by the READER.Displayed in a GUI (1300) are three images, namely: a Brightfield image(1310), a fluorescence image (1320). Grids, extracted and aligned by theREADER, also are displayed.

Example 4 Analyzing Images and Extracting Representations

FIG. 14 illustrates the Decoded Assay Data Record in two sections,namely: a text display (1400) listing assay signals extracted from theassay data record along with tag indices assigning each signal to acluster and hence to a color code; and, a bar graph display (1420) ofdata from the Decoded Assay Data Record.

It should be understood that the foregoing examples and descriptions areexemplary only and not limiting, and that all methods and processes setforth are not to be limited to any particular order or sequence, unlessspecified, and that the scope of the invention is defined only in theclaims which follow, and includes all equivalents of the claims.

1. A method of constructing a grid for use in aligning two or moreimages, where said images reflect corresponding arrays of signals from(i) an array of encoded beads, such that an encoding signal reflects theencoding of the beads, or (ii) the array of encoded beads haveparticular ligands attached to particular beads, and wherein the ligandsattached to beads are identifiable by the encoding and whereinparticular ligands are capable of binding to particular targets, andwherein following binding to targets an assay signal is associated withthose beads which have attached ligands bound to targets, comprising:applying an algorithm to the array which finds the shortest paths amongthe local intensity minima so as to delineate the local intensitymaximum associated with a signal in the array and thereby define a gridas an optimal path as follows: computing an external gradient image bysubtracting a dilated image from an original image of the signals;determining overall orientation by: (i) establishing horizontal andvertical reference lines, (ii) computing the shortest paths along twodirections, (iii) computing a ratio by dividing each shortest pathlength by the length of the corresponding reference line; and (iv)selecting the reference line yielding the ratio closest to unity todetermine the overall orientation; finding the horizontal grid partitionby shifting the reference line by one grid unit to a new position andcomputing the shortest path, and continuing said shifting and computinguntil the shifted reference line falls outside the array boundary;finding the vertical grid partition by finding the shortest path along adiagonal direction, and computing intersections of this diagonal pathand every horizontal partition, provided that, given the intersectionsof the diagonal partition and two consecutive horizontal partitions, thevertical partition will be located at the midpoint of theseintersections; and using the grid to differentiate individual assaysignals or individual encoding signals so as to identify which ligandsbound to targets.
 2. The method of claim 1 wherein the algorithm isDijkstra's “shortest path” algorithm.
 3. The method of any of claims 1or 2 wherein following construction of the grid, the grid is grown orshrunk to the expected array boundary.
 4. The method of claim 3 whereingrid stagger is corrected.
 5. The method of any of claims 1 or 2 whereinthe grid coordinates are stored in a file.
 6. The method of claims 1 or2 wherein the algorithm is applied by a programmed computer.